What makes an AI discovery conversation useful rather than generic?

Short answer

Tie each question to the client's actual workflow, responsibility and consequence. The aim is an accurate activity description and professional review, not a score implying underwriting or guaranteed coverage.

An illustrative example

A vague AI questionnaire becomes a concrete service discussion.

This is a hypothetical situation, not a real customer outcome or a coverage determination.

Three facts to prepare

• Actual workflow
• Responsible parties
• Failure consequence

Use a brief, accurate summary. Separate confirmed facts from assumptions; keep passwords, identity numbers, private customer records and confidential documents out of an initial marketplace request.

A question to bring to the right professional

Which facts change the review for this specific client?

The goal is an accurate client activity description and a qualified review of available terms. Keep professional, legal and technical questions with their appropriate reviewers. Use category-level information first and obtain evidence through a secure process, avoiding unsupported assumptions about compliance, authority or coverage.

Sources and scope

• NIST: AI Risk Management Framework

  A voluntary framework for organizing AI risk. It is not an insurance contract, certification or determination of legal compliance.

• Travelers: technology errors and omissions risk examples

  Insurer-authored context on technology service allegations and differences in policy wording. Product descriptions apply to that source; they are not LunarQuote offers or evidence of a partnership.

Sources supply the stated background, not a determination about the illustrative case. The example, checklist and discussion prompt are LunarQuote educational material. Source links checked October 5, 2026.